POST /v1/embeddings — Generate Vector Embeddings

The /v1/embeddings endpoint converts text into dense numerical vectors that capture semantic meaning. Use embeddings to power semantic search, Retrieval-Augmented Generation (RAG) pipelines, document clustering, duplicate detection, and cross-lingual similarity tasks. The endpoint is OpenAI-compatible, so any library or framework that targets the OpenAI embeddings API works out of the box by pointing base_url at Meliai. Embedding requests default to the :price routing flavor, keeping bulk vectorisation costs low.

Endpoint

POST https://api.meliai.ai/v1/embeddings

Authorization: Bearer sk-mel-<KEY> via Authorization header.

Parameters

string

required

The embedding model ID to use. Check GET /v1/models for available embedding models. Routing flavor suffixes (e.g. :speed) are supported but :price is applied by default for embedding requests.

string | array

required

The text to embed. Pass a single string or an array of strings to embed multiple texts in one request. Batching multiple inputs in a single call is more efficient than sending them individually.

string

Format of the returned vectors:

"float" — array of 64-bit floats (default)
"base64" — base64-encoded binary representation, useful for reducing response payload size

Example

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ["MELIAI_API_KEY"],
    base_url="https://api.meliai.ai/v1",
)

response = client.embeddings.create(
    model="<EMBEDDING_MODEL_ID>",
    input=["European AI infrastructure", "sovereign data residency"],
)

for item in response.data:
    print(f"Index {item.index}: vector dim {len(item.embedding)}")

import OpenAI from "openai";

const client = new OpenAI({
  apiKey: process.env.MELIAI_API_KEY,
  baseURL: "https://api.meliai.ai/v1",
});

const response = await client.embeddings.create({
  model: "<EMBEDDING_MODEL_ID>",
  input: ["European AI infrastructure", "sovereign data residency"],
});

for (const item of response.data) {
  console.log(`Index ${item.index}: vector dim ${item.embedding.length}`);
}

curl https://api.meliai.ai/v1/embeddings \
  -H "Authorization: Bearer $MELIAI_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "<EMBEDDING_MODEL_ID>",
    "input": ["European AI infrastructure", "sovereign data residency"]
  }'

Response

{
  "object": "list",
  "model": "<EMBEDDING_MODEL_ID>",
  "data": [
    {
      "object": "embedding",
      "index": 0,
      "embedding": [0.0023, -0.0147, 0.0382, "..."]
    },
    {
      "object": "embedding",
      "index": 1,
      "embedding": [0.0051, -0.0093, 0.0271, "..."]
    }
  ],
  "usage": {
    "prompt_tokens": 8,
    "total_tokens": 8
  },
  "environment_impact": {
    "energy_kwh": 0.000008,
    "carbon_g_co2": 0.0006,
    "water_liters": 0.00003,
    "renewable_percent": 91,
    "pue": 1.2,
    "provider_id": "provider-eu-central",
    "location": "Munich, DE"
  },
  "billing_cost": {
    "energy": 0.000004,
    "credits": 0.000062,
    "paid_with": "credits"
  }
}

array

Ordered array of embedding objects, one per input string. Each contains:

object — always "embedding"
index — position of this item in the input array
embedding — the vector as an array of floats (or a base64 string if encoding_format is "base64")

object

Token counts for the request: prompt_tokens and total_tokens.

object

Per-request environmental footprint. Fields: energy_kwh, carbon_g_co2, water_liters, renewable_percent, pue, provider_id, location.

object

Itemised cost: energy (EUR), credits deducted, and paid_with.

Embedding requests automatically use the :price routing flavor. If you need lower latency for real-time retrieval, append :speed to your model ID: "<EMBEDDING_MODEL_ID>:speed".

All embedding computation runs on European infrastructure. Input text never leaves the EU and is never used to train models.

Overview

Inference

Batch & Files

Models

POST /v1/embeddings — Generate Vector Embeddings

Endpoint

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Example

Response

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